Machine vision and pattern recognition algorithms are of interest for applications related to climate change monitoring, change detection, and Land Use/Land Cover (LULC) classification using satellite image data. However, these approaches frequently are not robust for multiple classes that are spatially mixed. Furthermore, despite the vast archives of globally distributed remotely sensed data collected over the last four decades and the availability of computing resources to process these datasets, global assessment of all but the simplest landscape features is not currently possible.
A fundamental problem to creating scalable feature extraction technology capable of processing imagery datasets at global scales is the overconstrained training needed to generate effective solutions. Many features of environmental importance including, but not limited to, rivers, water bodies, coastlines, glaciers, and vegetation boundaries, are readily recognizable to humans based on a simple set of attributes. The very best of current feature extraction software, e.g., the Los Alamos National Laboratory-developed GeniePro™, however, requires extensive, image-specific training that leads to a solution with limited applicability to images other than the image used for training.
Accordingly, developing automatic, unsupervised feature extraction and high-resolution, pixel-level classification tools that do not require overconstrained training may be beneficial and have a significant impact for a number of application areas, e.g., for studying climate change effects and providing the climate change community with more exact ways of detecting yearly and seasonal changes.